Debt-to-GDP Ratio Is Increased by Unemployment, Stock Trading, and Inequality, Decreased by Inflation, and Unaffected by Interest Rates
Bibliographic record
Abstract
We demonstrate that the debt-to-GDP ratio is well predicted by five closely watched variables that include inflation. The Federal Reserve, economists, and stock market traders have recently expressed concern about the "the worst inflation in 100 years" (CNBC and Aljazeera, 20 May, 2022). Despite their semantic massage, we demonstrate that this outbreak occurred long after the time series studies here, indicating that inflation drove our debt-to-GDP ratios well before it broke out of control in 2022. This suggests that inflation may be an endemic and uncontrollable phenomenon. We contradict the growing concern about “the worst inflation in 100 years” by showing that inflation lowers the debt-to-GDP ratio. Our data driven discovery (DDD) shows that the debt-to-GDP ratio, acting as a dependent variable, is increased by unemployment, stock trading, and inequality, decreased by inflation, and unaffected by interest rates, all acting as independent variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".